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PolygraphLLM

Open-Source Hallucination Detection & Factuality Evaluation Toolkit for LLMs

What is polygraphLLM?

PolygraphLLM is an open-source toolkit designed to detect hallucinations and evaluate the factuality of outputs from Large Language Models (LLMs). Hallucinations are plausible-sounding but incorrect or fabricated statements generated by LLMs, posing risks in high-stakes applications such as healthcare, law, and finance. This toolkit offers state-of-the-art methods to benchmark, visualize, and improve the reliability of LLM-generated content.

Why Hallucination Detection & Factuality Evaluation Matter

Financial and Reputational Liability

Air Canada faced legal penalties when its chatbot hallucinated a refund policy. Financial services risk regulatory fines from incorrect LLM analyses. The 2024 LM-Polygraph benchmark found hallucination rates of 3–10% in critical domains—equivalent to thousands of high-risk errors monthly at enterprise scale.

Regulatory Compliance Imperatives

With regulations like the EU AI Act and SEC guidance, organizations must demonstrate hallucination mitigation. polygraphLLM helps meet these mandates through robust detection and evaluation capabilities.

Erosion of Public Trust

Survey data shows over 60% of consumers distrust AI due to hallucination risks. Incidents like the 2023 Bing chatbot missteps have tangible reputational impact, increasing user churn and undermining adoption.

Operational Efficiency Demands

Verifying outputs manually is costly—estimated at $27.50/hour. polygraphLLM helps reduce these overheads, enabling scalable and trusted LLM deployment.

How Does polygraphLLM Work?

  • Implements both black-box and white-box hallucination detection methods.
  • Supports diverse NLP tasks: QA, summarization, translation, and more.
  • Provides a unified Python API and interactive visualization dashboard.
  • Includes Semantic Nearest Neighbor Entropy (SNNE) and Weighted SNNE (WSNNE) for robust semantic uncertainty quantification.
pip install polygraphllm

from polygraph import SemanticValidator

validator = SemanticValidator (model='gpt-4')
results = validator. detect_hallucinations (prompt, generations)

Related Paper

Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity Dang Nguyen (UCLA), Ali Payani (Cisco Systems), Baharan Mirzasoleiman (UCLA)

This paper introduces SNNE, a method that generalizes semantic entropy by using pairwise semantic similarity between generations. It significantly improves hallucination detection in LLMs and can be extended to white-box models.

Demo

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